Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5497533 | 0.0405674 | 0.95 | 0.4697120 | 0.6252302 | HDI | conditional | Bayesian R-squared |
| 0.1485675 | 0.1553459 | 0.95 | 0.0000233 | 0.4005399 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5627202 | 0.0392795 | 0.95 | 0.4761295 | 0.6325768 | HDI | conditional | Bayesian R-squared |
| 0.1678926 | 0.1764874 | 0.95 | 0.0000337 | 0.4325012 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5533693 | 0.0430389 | 0.95 | 0.4576215 | 0.6251421 | HDI | conditional | Bayesian R-squared |
| 0.1115205 | 0.1229691 | 0.95 | 0.0000083 | 0.3349325 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5557305 | 0.0412885 | 0.95 | 0.4682730 | 0.6318487 | HDI | conditional | Bayesian R-squared |
| 0.1147872 | 0.1272852 | 0.95 | 0.0000009 | 0.3509720 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5491595 | 0.0421747 | 0.95 | 0.4577560 | 0.6202150 | HDI | conditional | Bayesian R-squared |
| 0.1908664 | 0.1805869 | 0.95 | 0.0000139 | 0.4365256 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5527765 | 0.0440311 | 0.95 | 0.4599328 | 0.6260697 | HDI | conditional | Bayesian R-squared |
| 0.0931940 | 0.1047355 | 0.95 | 0.0000360 | 0.3077291 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5575006 | 0.0421702 | 0.95 | 0.4691356 | 0.6348832 | HDI | conditional | Bayesian R-squared |
| 0.1051653 | 0.1160475 | 0.95 | 0.0000061 | 0.3619841 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5570286 | 0.0415047 | 0.95 | 0.4700102 | 0.6303055 | HDI | conditional | Bayesian R-squared |
| 0.0963793 | 0.1117034 | 0.95 | 0.0000222 | 0.3161430 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5566551 | 0.0392546 | 0.95 | 0.4667264 | 0.6266153 | HDI | conditional | Bayesian R-squared |
| 0.1009053 | 0.1136120 | 0.95 | 0.0000138 | 0.3382319 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5563844 | 0.0415695 | 0.95 | 0.4682576 | 0.6258493 | HDI | conditional | Bayesian R-squared |
| 0.0921855 | 0.1076866 | 0.95 | 0.0000087 | 0.3367914 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5564038 | 0.0405336 | 0.95 | 0.4662464 | 0.6287134 | HDI | conditional | Bayesian R-squared |
| 0.1107339 | 0.1191393 | 0.95 | 0.0000176 | 0.3397985 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5528571 | 0.0409670 | 0.95 | 0.460947 | 0.6280460 | HDI | conditional | Bayesian R-squared |
| 0.0816878 | 0.0958526 | 0.95 | 0.000013 | 0.2893218 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5627626 | 0.0380185 | 0.95 | 0.4777328 | 0.6263958 | HDI | conditional | Bayesian R-squared |
| 0.2588924 | 0.2354025 | 0.95 | 0.0000670 | 0.5218313 | HDI | marginal | Bayesian R-squared |
Posterior draws
Model output
##
## Model Info:
## function: stan_glmer
## family: Gamma [log]
## formula: stage_rise_cm ~ MI60_mmhr * lag_pwd + fire + sp_cat + yearCont +
## (1 + MI60_mmhr | site_number) + (1 | yearFact)
## algorithm: sampling
## sample: 4500 (posterior sample size)
## priors: see help('prior_summary')
## observations: 247
## groups: site_number (26), yearFact (3)
##
## Estimates:
## mean sd 10% 50% 90%
## (Intercept) 1.2 0.9 -0.1 1.4 2.1
## MI60_mmhr 0.5 0.1 0.4 0.5 0.6
## lag_pwd -0.1 0.1 -0.3 -0.1 0.1
## fireetf 1.5 0.4 1.0 1.5 1.9
## sp_catlow 0.5 0.4 0.0 0.5 1.0
## yearCont 0.0 0.5 -0.7 0.0 0.6
## MI60_mmhr:lag_pwd 0.1 0.1 0.0 0.1 0.3
## b[(Intercept) site_number:1] -0.8 0.4 -1.4 -0.8 -0.3
## b[MI60_mmhr site_number:1] -0.3 0.2 -0.6 -0.3 -0.1
## b[(Intercept) site_number:2] -1.0 0.3 -1.4 -1.0 -0.5
## b[MI60_mmhr site_number:2] -0.4 0.2 -0.6 -0.4 -0.1
## b[(Intercept) site_number:3] -0.5 0.3 -0.9 -0.5 -0.1
## b[MI60_mmhr site_number:3] -0.2 0.2 -0.4 -0.2 0.0
## b[(Intercept) site_number:4] -0.5 0.5 -1.2 -0.6 0.1
## b[MI60_mmhr site_number:4] 0.0 0.4 -0.4 0.0 0.4
## b[(Intercept) site_number:5] 0.0 0.3 -0.3 0.0 0.4
## b[MI60_mmhr site_number:5] 0.1 0.2 -0.1 0.1 0.4
## b[(Intercept) site_number:6] 0.9 0.3 0.5 0.9 1.3
## b[MI60_mmhr site_number:6] 0.0 0.3 -0.3 0.0 0.3
## b[(Intercept) site_number:7] 0.2 0.3 -0.3 0.1 0.6
## b[MI60_mmhr site_number:7] 0.0 0.3 -0.5 0.0 0.3
## b[(Intercept) site_number:8] 0.1 0.5 -0.5 0.1 0.8
## b[MI60_mmhr site_number:8] -0.1 0.4 -0.6 -0.1 0.3
## b[(Intercept) site_number:9] 1.3 0.5 0.7 1.3 1.9
## b[MI60_mmhr site_number:9] 0.0 0.4 -0.6 0.0 0.4
## b[(Intercept) site_number:10] 0.4 0.5 -0.2 0.4 1.0
## b[MI60_mmhr site_number:10] 0.1 0.3 -0.3 0.1 0.5
## b[(Intercept) site_number:11] 0.7 0.4 0.2 0.6 1.1
## b[MI60_mmhr site_number:11] 0.5 0.3 0.1 0.5 1.0
## b[(Intercept) site_number:12] 0.0 0.4 -0.5 0.0 0.5
## b[MI60_mmhr site_number:12] -0.1 0.3 -0.5 -0.1 0.2
## b[(Intercept) site_number:13] 0.6 0.4 0.1 0.6 1.0
## b[MI60_mmhr site_number:13] 0.0 0.2 -0.3 0.0 0.3
## b[(Intercept) site_number:14] 1.1 0.4 0.6 1.1 1.6
## b[MI60_mmhr site_number:14] 0.0 0.4 -0.5 0.0 0.4
## b[(Intercept) site_number:15] 0.2 0.4 -0.3 0.2 0.6
## b[MI60_mmhr site_number:15] -0.2 0.2 -0.5 -0.2 0.1
## b[(Intercept) site_number:16] 0.5 0.3 0.1 0.5 0.9
## b[MI60_mmhr site_number:16] 0.1 0.3 -0.2 0.1 0.5
## b[(Intercept) site_number:17] 0.3 0.4 -0.2 0.3 0.7
## b[MI60_mmhr site_number:17] -0.2 0.3 -0.5 -0.1 0.1
## b[(Intercept) site_number:18] -1.0 0.4 -1.6 -1.0 -0.5
## b[MI60_mmhr site_number:18] -0.1 0.4 -0.5 -0.1 0.3
## b[(Intercept) site_number:19] -0.4 0.3 -0.9 -0.4 0.0
## b[MI60_mmhr site_number:19] 0.2 0.3 -0.1 0.2 0.6
## b[(Intercept) site_number:20] 0.9 0.3 0.4 0.8 1.3
## b[MI60_mmhr site_number:20] 0.4 0.3 0.1 0.3 0.7
## b[(Intercept) site_number:21] -1.3 0.4 -1.8 -1.3 -0.8
## b[MI60_mmhr site_number:21] 0.0 0.4 -0.4 0.0 0.5
## b[(Intercept) site_number:22] -0.2 0.3 -0.6 -0.2 0.2
## b[MI60_mmhr site_number:22] 0.1 0.2 -0.2 0.1 0.4
## b[(Intercept) site_number:23] 0.5 0.4 0.0 0.5 0.9
## b[MI60_mmhr site_number:23] 0.4 0.3 0.0 0.3 0.7
## b[(Intercept) site_number:24] 0.1 0.4 -0.4 0.1 0.6
## b[MI60_mmhr site_number:24] 0.2 0.2 -0.1 0.1 0.5
## b[(Intercept) site_number:25] 0.6 0.4 0.1 0.6 1.1
## b[MI60_mmhr site_number:25] 0.0 0.3 -0.3 0.0 0.3
## b[(Intercept) site_number:26] -1.0 0.4 -1.5 -1.0 -0.6
## b[MI60_mmhr site_number:26] -0.1 0.2 -0.4 -0.1 0.1
## b[(Intercept) yearFact:2021] 0.5 1.0 -0.3 0.2 2.0
## b[(Intercept) yearFact:2022] 0.8 0.9 0.0 0.5 2.1
## b[(Intercept) yearFact:2023] 0.6 1.0 -0.2 0.3 2.1
## shape 2.2 0.2 1.9 2.2 2.4
## Sigma[site_number:(Intercept),(Intercept)] 0.7 0.3 0.4 0.6 1.0
## Sigma[site_number:MI60_mmhr,(Intercept)] 0.1 0.1 0.0 0.1 0.2
## Sigma[site_number:MI60_mmhr,MI60_mmhr] 0.1 0.1 0.0 0.1 0.3
## Sigma[yearFact:(Intercept),(Intercept)] 2.5 5.5 0.0 0.5 6.9
##
## Fit Diagnostics:
## mean sd 10% 50% 90%
## mean_PPD 24.1 2.3 21.2 23.9 27.1
##
## The mean_ppd is the sample average posterior predictive distribution of the outcome variable (for details see help('summary.stanreg')).
##
## MCMC diagnostics
## mcse Rhat n_eff
## (Intercept) 0.0 1.0 1324
## MI60_mmhr 0.0 1.0 2723
## lag_pwd 0.0 1.0 4699
## fireetf 0.0 1.0 1706
## sp_catlow 0.0 1.0 1807
## yearCont 0.0 1.0 3313
## MI60_mmhr:lag_pwd 0.0 1.0 2262
## b[(Intercept) site_number:1] 0.0 1.0 3117
## b[MI60_mmhr site_number:1] 0.0 1.0 3346
## b[(Intercept) site_number:2] 0.0 1.0 2901
## b[MI60_mmhr site_number:2] 0.0 1.0 3283
## b[(Intercept) site_number:3] 0.0 1.0 1947
## b[MI60_mmhr site_number:3] 0.0 1.0 3288
## b[(Intercept) site_number:4] 0.0 1.0 5186
## b[MI60_mmhr site_number:4] 0.0 1.0 6284
## b[(Intercept) site_number:5] 0.0 1.0 2398
## b[MI60_mmhr site_number:5] 0.0 1.0 3404
## b[(Intercept) site_number:6] 0.0 1.0 2659
## b[MI60_mmhr site_number:6] 0.0 1.0 5372
## b[(Intercept) site_number:7] 0.0 1.0 3192
## b[MI60_mmhr site_number:7] 0.0 1.0 6123
## b[(Intercept) site_number:8] 0.0 1.0 2627
## b[MI60_mmhr site_number:8] 0.0 1.0 4163
## b[(Intercept) site_number:9] 0.0 1.0 2944
## b[MI60_mmhr site_number:9] 0.0 1.0 3202
## b[(Intercept) site_number:10] 0.0 1.0 1768
## b[MI60_mmhr site_number:10] 0.0 1.0 5795
## b[(Intercept) site_number:11] 0.0 1.0 2728
## b[MI60_mmhr site_number:11] 0.0 1.0 2144
## b[(Intercept) site_number:12] 0.0 1.0 2223
## b[MI60_mmhr site_number:12] 0.0 1.0 4112
## b[(Intercept) site_number:13] 0.0 1.0 3135
## b[MI60_mmhr site_number:13] 0.0 1.0 4484
## b[(Intercept) site_number:14] 0.0 1.0 3205
## b[MI60_mmhr site_number:14] 0.0 1.0 4359
## b[(Intercept) site_number:15] 0.0 1.0 2031
## b[MI60_mmhr site_number:15] 0.0 1.0 2164
## b[(Intercept) site_number:16] 0.0 1.0 2671
## b[MI60_mmhr site_number:16] 0.0 1.0 5278
## b[(Intercept) site_number:17] 0.0 1.0 2106
## b[MI60_mmhr site_number:17] 0.0 1.0 3334
## b[(Intercept) site_number:18] 0.0 1.0 3901
## b[MI60_mmhr site_number:18] 0.0 1.0 5442
## b[(Intercept) site_number:19] 0.0 1.0 2622
## b[MI60_mmhr site_number:19] 0.0 1.0 3741
## b[(Intercept) site_number:20] 0.0 1.0 2817
## b[MI60_mmhr site_number:20] 0.0 1.0 2828
## b[(Intercept) site_number:21] 0.0 1.0 3227
## b[MI60_mmhr site_number:21] 0.0 1.0 3600
## b[(Intercept) site_number:22] 0.0 1.0 2900
## b[MI60_mmhr site_number:22] 0.0 1.0 5902
## b[(Intercept) site_number:23] 0.0 1.0 2553
## b[MI60_mmhr site_number:23] 0.0 1.0 2735
## b[(Intercept) site_number:24] 0.0 1.0 3432
## b[MI60_mmhr site_number:24] 0.0 1.0 3478
## b[(Intercept) site_number:25] 0.0 1.0 3523
## b[MI60_mmhr site_number:25] 0.0 1.0 5163
## b[(Intercept) site_number:26] 0.0 1.0 3058
## b[MI60_mmhr site_number:26] 0.0 1.0 4979
## b[(Intercept) yearFact:2021] 0.0 1.0 1592
## b[(Intercept) yearFact:2022] 0.0 1.0 1229
## b[(Intercept) yearFact:2023] 0.0 1.0 1520
## shape 0.0 1.0 3950
## Sigma[site_number:(Intercept),(Intercept)] 0.0 1.0 1596
## Sigma[site_number:MI60_mmhr,(Intercept)] 0.0 1.0 2205
## Sigma[site_number:MI60_mmhr,MI60_mmhr] 0.0 1.0 1269
## Sigma[yearFact:(Intercept),(Intercept)] 0.1 1.0 2084
## mean_PPD 0.0 1.0 4873
## log-posterior 0.3 1.0 788
##
## For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).
| R2 | SD | CI | CI_low | CI_high | CI_method | Component | Effectsize |
|---|---|---|---|---|---|---|---|
| 0.5573937 | 0.0430702 | 0.95 | 0.4675577 | 0.6320833 | HDI | conditional | Bayesian R-squared |
| 0.0817641 | 0.0894100 | 0.95 | 0.0000151 | 0.2862768 | HDI | marginal | Bayesian R-squared |
Posterior draws